The document discusses how to use the DataWeave transformer in Anypoint Studio to transform messages. The DataWeave transformer allows writing transformations using the DataWeave language. It takes the incoming message elements as inputs and performs actions to produce an output message. The editor provides autocomplete, output previews, and generates .dwl files to store the transformation code. Multiple outputs can be defined by adding tabs in the transform section.

2. Using the DataWeave Transformer
 In Anypoint Studio, you can place a Transform
Message element in a flow to create transformations
using the DataWeave language.
 The editor helps you do this by offering intelligent
autocomplete suggestions, an output preview that is
updated in real time as you make changes.
 This generates a .dwl transformation file (or several of
them) that stores your code and is packaged with your
Mule application.

3.  When adding a Transform Message element to a Mule
Flow, it takes the elements from the incoming Mule
Message as its inputs. It then performs the necessary
actions to produce a Mule message as output for the
next element in the flow

4.  The DataWeave Text Editor GUI
 The Transform Message element allows you to freely
write DataWeave code.
 If you click on an instance of the Transform Message
element in your flow, its properties editor will be
displayed. This editor is divided into three sections:
 Input
 Transform
 Output

5. Input Section
In the main tab, a tree view shows the known metadata
contents of the incoming Mule Message, allowing you to
explore it and know what data is available for using as an
input, and how to reference each part of it.
If the Mule flow doesn’t expose Metadata about the
elements you need from the incoming message, you can
manually specify it. To do so, select the element of the
input you desire to define, note that the pencil icon at
the top right is no longer grayed out. Click this icon to
open a new tab in your input section where you can
define a sample with the structure of this data.

6. When the input is of JSON or XML types, the sample input contains plain XML or JSON
code. When the input is of type POJO or DataWeave, the sample input is written
in DataWeave for more simplicity. In these cases the sample DataWeave code is merely a
way to display the sample data, not a transformation in itself.

7. Setting Reader Parameters
 Some input formats, like CSV, allow you to define a
reader with specific properties that make
DataWeave parse inputs differently. Select the
input element you wish to configure on the tree
view of the input section, then click the gear icon.

8. Transform Section
 you write the actual DataWeave code that carries out
the transform. Notice that changing the type of your
output directive changes the output section of the
editor. Although DataWeave as a language supports
adding input directives and naming these by any name
you like, in Studio the elements of the input message
are implicitly considered input directives and so they
don’t need to be defined in the header.

9. If Studio has any metadata about the components that
are upstream or downstream from your Transform
Message element at the time when you add the
component to your flow, a scaffolding for
your DataWeave code is written out automatically, with
as much depth as Studio can intelligently deduce. In
some cases, this code may be enough to carry out the
transformation you need, and no additional coding is
needed. Sometimes, all you need to do is fill in the blank
spaces in the scaffolding with references to the input
fields, other times you may want to carry out more
complex operations that involve aggregation, filtering,
calculations, defining custom functions, etc and there
you must write this out in DataWeave code.

10. Re-scaffolding Once you’ve added the Transform Message element
to your flow, any further changes you make to the
surrounding message processors and their metadata
won’t affect your `.dw`l file. You may still click the
Scaffolding button on the top left of the DataWeave
properties editor any time you want and have a new
scaffolding built, note that this will erase anything
you’ve written in the DataWeave body. Doing this
won’t affect any directives you defined in your header
(except for the output directive). Use this button if
you’ve made any changes to elements that come after
the Transform Message element on the flow that
expose metadata and don’t mind loosing what you’ve
already written into the transform’s body.

11. Referencing Existing Transforms
Instead of defining a new .dwl file, you can reference an
existing one by clicking theData Source button,
selecting On File as the source and referencing the
correct file.

12. Handling Multiple Outputs
A single Transform Message element can give shape to
several different components of the output Mule
message. Each of these output components must be
defined in a separate .dwl file, written out in a separate
tab of the Transform section. For example in one tab you
may be defining the payload contents, in another those
of an outbound property, and these will both be parts of
the same output Mule message

13. To add a new output, click the Plus sign at the
bottom right of the section:new+output.png[image]
A new tab will then appear, there you can specify
where in the output Mule message to place the
output of this DataWeave transform. In case you’re
creating a new variable or property, you must also
set a name for it.

14. In the XML editor you can do the same by adding
multiple child elements inside thedw:transform-
message component.
<dw:transform-message>
<dw:set-payload resource="classpath:path/transform.dwl"/> <dw:set-
variable variableName="myVariable"
resource="classpath:path/transform.dwl"/> <dw:set-session-variable
variableName="mySessionVariable"
resource="classpath:path/transform.dwl"/> </dw:transform-message>

15. Output Section
 This section has two tabs, one of them shows you a neat
expandable tree view of the output data structure, the
other shows you a preview of what the actual output looks
like, built from any sample data you provide in the input
section. As you make changes in the transport section,
notice how the data structure changes. The output of the
transformer is made into the selected component of the
output mule message. If your transformer has multiple
outputs, the Preview tab will display the one
corresponding to the currently selected transform.

16. Using DataWeave Language
Elsewhere
 All components in Mule that support Mule Expression
Language also support expressions written in DataWeave
Language. To invoke an expression written in DataWeave
language, simply invoke the dw() function, the expression
will return whatever the transform outputs.
 DataWeave expressions defined within this function work
just as those defined within a Transform Message element,
the only difference is that the output is returned into the
expression’s result, wherever it may be.

17.  For example, you can define a custom object and
populate it with elements from the payload:
 That same expression could be added inside a Logger,
 within a MEL expression, to print out its result:
dw(myobject:{id:payload.accountid, user:payload.user})
#[`dw(myobject:{id:payload.accountid, user:payload.user})`]